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Segmentation of magnetic resonance images is medically complex and important for study and diagnosis of medical brain images, because of its sensitivity in terms of noise for brain medical images. These are the main issues in classification of brain images. Because of uncertainty & vagueness of brain medical images, so that rough sets, fuzzy sets and Rough sets are mathematical tools evaluate and handle uncertainty and vagueness in medical brain images. Traditionally, different type of fuzzy sets, Rough sets and rough sets based approaches were introduced, they have different several drawbacks with respect to different parameters. So this paper introduces a novel image segmentation calculation method i.e. Enhanced and Explored Intuitionistic Rough based Fuzzy C-means Approach (EEISFCMA) with estimation of weight bias parameter for brain image segmentation. Intuitionistic Rough based fuzzy sets are generalized form of fuzzy, Rough sets and their representative elements are evaluated with non-membership and membership value. Proposed algorithm of this paper consists standard features of existing clustering without spatial weight context data, it defines sensitive of noise in brain images, so that our proposed algorithm deals with intensity and noise reduction of brain image effectively. Furthermore, to reduce iterations in clustering, proposed algorithm initializes cluster centroid based on weight measure using max-dist evaluation method before execution of proposed algorithm. Experimental results of proposed approach carried out efficient image segmentation results compared to existing segmented approaches developed in brain image and other related images. Mainly proposed approach have consists better experimental evaluation based on results.

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